Pre-validation Revisited
Abstract
Pre-validation is a way to build prediction model with two datasets of significantly different feature dimensions. Previous work showed that the asymptotic distribution of the resulting test statistic for the pre-validated predictor deviates from a standard Normal, hence leads to issues in hypothesis testing. In this paper, we revisit the pre-validation procedure and extend the problem formulation without any independence assumption on the two feature sets. We propose not only an analytical distribution of the test statistic for the pre-validated predictor under certain models, but also a generic bootstrap procedure to conduct inference. We show properties and benefits of pre-validation in prediction, inference and error estimation by simulations and applications, including analysis of a breast cancer study and a synthetic GWAS example.
Cite
@article{arxiv.2505.14985,
title = {Pre-validation Revisited},
author = {Jing Shang and Sourav Chatterjee and Trevor Hastie and Robert Tibshirani},
journal= {arXiv preprint arXiv:2505.14985},
year = {2025}
}